AI Red Teaming for Agents, LLM Apps and MCP: Find exploitable AI weaknesses before they become incidents.
The Assessment Engine tests supported AI systems out-of-band using adaptive text, image, audio and video attack surfaces, without sitting in the production traffic path.
Adaptive, not a fixed checklist.
The engine does not simply replay a static list of prompts. It learns from how the target responds, identifies which attack paths are landing, and develops the test further from there.
Generated attacks
Tests are built from the target’s role and responses, not only from a predefined prompt list.
Context-aware judging
The evaluator considers purpose, input, attack and output together to reduce meaningless findings.
Multimodal pressure
Test text, image, audio and video attack surfaces when the underlying model supports them.
What it tests
Risky interaction
A text or image prompt attempts to manipulate the system into exposing data or using an unauthorized tool.
Assessment
Adaptive red-teaming tests the attack path out-of-band and follows the system response.
Evidence
The finding records what was tested, what happened and the evidence behind the result; the test can be repeated after a change.
Agents
Prompt injection, exfiltration, permissions, tool misuse and supply-chain patterns.
Models and model APIs
Adversarial testing over common model and REST-style interfaces, including private inference.
MCP servers
Tool enumeration, permission analysis, input validation and MCP-specific attack patterns.
Skills and repositories
Inspect code and markdown artifacts and, where appropriate, probe behavior in an isolated environment.
A finding should tell you what to do next.
Each finding should answer four things: what attack worked, what the system did, what business impact it could create, and how to remediate it. Then the fix can be re-tested instead of assumed.
- What attack worked
- What the system did
- Business impact
- How to remediate
Continuous, not annual.
AI systems change constantly: prompts are tuned, tools are added and models are replaced. Assessment can be repeated around releases and changes so the security posture follows the system, not last quarter’s snapshot.
Frequently asked questions
What does Find It test?
Find It runs adaptive adversarial tests against agents, models, MCP servers and skills. It supports black-box and multimodal testing across text, image, audio and video.
How is this different from a fixed prompt-injection checklist?
The attack engine adapts to how the target actually responds instead of working through a fixed list of prompts. The goal is reproducible evidence of exploitable behavior, not a flag for a theoretical risk.
Which attacks can Find It identify?
Find It covers prompt injection, sensitive-data and PII leakage, unauthorized tool use, MCP exposure and blast radius, agent behavior outside intended boundaries, malicious or unsafe skills, and multimodal adversarial inputs. Across these categories, Find It covers 50+ attack vectors.
Which frameworks do findings map to?
Findings can be mapped to the EU AI Act, OWASP LLM Top 10, OWASP Agentic AI Top 10, NIST AI RMF, MITRE ATLAS, MAESTRO and ISO/IEC 42001. You can also add your own compliance documents, internal policies, governance requirements and organization-specific rules, and HikmaAI incorporates them into the policies and controls your agents are expected to follow.
Can we test before production?
Yes. Assessment runs out of band, so it does not need to sit in the live traffic path. Teams can establish a baseline, remediate findings and then re-test to verify the fix.
Does Find It provide remediation guidance?
Yes. Findings include evidence and remediation guidance, so engineering and security teams can move from a discovered weakness to a verified fix.
See how a supported assessment turns a risky interaction into an evidence-backed finding and a repeatable validation step.